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Streamlining follicular monitoring during controlled ovarian stimulation. A data-driven approach to efficient IVF care in the new era of social distancing

Streamlining follicular monitoring during controlled ovarian stimulation. A data-driven approach to efficient IVF care in the new era of social distancing
Streamlining follicular monitoring during controlled ovarian stimulation. A data-driven approach to efficient IVF care in the new era of social distancing
STUDY QUESTION
What is the optimal follicular tracking strategy for controlled ovarian stimulation (COS) in order to minimise face-to-face interactions?

SUMMARY ANSWER
As data from follicular tracking scans on Days 5, 6 or 7 of stimulation are the most useful to accurately predict trigger timing and risk of over-response, scans on these days should be prioritised if streamlined monitoring is necessary.

WHAT IS KNOWN ALREADY
British Fertility Society guidance for centres restarting ART following coronavirus disease 2019 (COVID-19) pandemic-related shutdowns recommends reducing the number of patient visits for monitoring during COS. Current evidence on optimal monitoring during ovarian stimulation is sparse, and protocols vary significantly. Small studies of simplifying IVF therapy by minimising monitoring have reported no adverse effects on outcomes, including live birth rate. There are opportunities to learn from the adaptations necessary during these extraordinary times to improve the efficiency of IVF care in the longer term.

STUDY DESIGN, SIZE, DURATION
A retrospective database analysis of 9294 ultrasound scans performed during monitoring of 2322 IVF cycles undertaken by 1875 women in a single centre was performed. The primary objective was to identify when in the IVF cycle the data obtained from ultrasound are most predictive of both oocyte maturation trigger timing and an over-response to stimulation. If a reduced frequency of clinic visits is needed due to COVID-19 precautions, prioritising attendance for monitoring scans on the most predictive cycle days may be prudent.

PARTICIPANTS/MATERIALS, SETTING, METHODS
The study comprised anonymised retrospective database analysis of IVF/ICSI cycles at a tertiary referral IVF centre. Machine learning models are used in combining demographic and follicular tracking data to predict cycle oocyte maturation trigger timing and over-response. The primary outcome was the day or days in cycle from which scan data yield optimal model prediction performance statistics. The model for predicting trigger day uses patient age, number of follicles at baseline scan and follicle count by size for the current scan. The model to predict over-response uses age and number of follicles of a given size.

MAIN RESULTS AND THE ROLE OF CHANCE
The earliest cycle day for which our model has high accuracy to predict both trigger day and risk of over-response is stimulation Day 5. The Day 5 model to predict trigger date has a mean squared error 2.16 ± 0.12 and to predict over-response an area under the receiver operating characteristic curve 0.91 ± 0.01.

LIMITATIONS, REASONS FOR CAUTION
This is a retrospective single-centre study and the results may not be generalisable to centres using different treatment protocols. The results are derived from modelling, and further clinical validation studies will verify the accuracy of the model.

WIDER IMPLICATIONS OF THE FINDINGS
Follicular tracking starting at Day 5 of stimulation may help to streamline the amount of monitoring required in COS. Previous small studies have shown that minimal monitoring protocols did not adversely impact outcomes. If IVF can safely be made less onerous on the clinic’s resources and patient’s time, without compromising success, this could help to reduce burden-related treatment drop-out.

STUDY FUNDING/COMPETING INTEREST(S)
F.P.C. acknowledges funding from the NIHR Applied Research Collaboration Wessex. The authors declare they have no competing interests in relation to this work.

TRIAL REGISTRATION NUMBER
N/A.
1460-2350
Robertson, Isla
b4e0c5be-ff75-4f0b-b519-9083b2c698a2
Chmiel, Francis
2de259aa-a5eb-460c-bfbf-8b44ed02e2bd
Cheong, Ying
4efbba2a-3036-4dce-82f1-8b4017952c83
Robertson, Isla
b4e0c5be-ff75-4f0b-b519-9083b2c698a2
Chmiel, Francis
2de259aa-a5eb-460c-bfbf-8b44ed02e2bd
Cheong, Ying
4efbba2a-3036-4dce-82f1-8b4017952c83

Robertson, Isla, Chmiel, Francis and Cheong, Ying (2020) Streamlining follicular monitoring during controlled ovarian stimulation. A data-driven approach to efficient IVF care in the new era of social distancing. Human Reproduction. (doi:10.1093/humrep/deaa251).

Record type: Article

Abstract

STUDY QUESTION
What is the optimal follicular tracking strategy for controlled ovarian stimulation (COS) in order to minimise face-to-face interactions?

SUMMARY ANSWER
As data from follicular tracking scans on Days 5, 6 or 7 of stimulation are the most useful to accurately predict trigger timing and risk of over-response, scans on these days should be prioritised if streamlined monitoring is necessary.

WHAT IS KNOWN ALREADY
British Fertility Society guidance for centres restarting ART following coronavirus disease 2019 (COVID-19) pandemic-related shutdowns recommends reducing the number of patient visits for monitoring during COS. Current evidence on optimal monitoring during ovarian stimulation is sparse, and protocols vary significantly. Small studies of simplifying IVF therapy by minimising monitoring have reported no adverse effects on outcomes, including live birth rate. There are opportunities to learn from the adaptations necessary during these extraordinary times to improve the efficiency of IVF care in the longer term.

STUDY DESIGN, SIZE, DURATION
A retrospective database analysis of 9294 ultrasound scans performed during monitoring of 2322 IVF cycles undertaken by 1875 women in a single centre was performed. The primary objective was to identify when in the IVF cycle the data obtained from ultrasound are most predictive of both oocyte maturation trigger timing and an over-response to stimulation. If a reduced frequency of clinic visits is needed due to COVID-19 precautions, prioritising attendance for monitoring scans on the most predictive cycle days may be prudent.

PARTICIPANTS/MATERIALS, SETTING, METHODS
The study comprised anonymised retrospective database analysis of IVF/ICSI cycles at a tertiary referral IVF centre. Machine learning models are used in combining demographic and follicular tracking data to predict cycle oocyte maturation trigger timing and over-response. The primary outcome was the day or days in cycle from which scan data yield optimal model prediction performance statistics. The model for predicting trigger day uses patient age, number of follicles at baseline scan and follicle count by size for the current scan. The model to predict over-response uses age and number of follicles of a given size.

MAIN RESULTS AND THE ROLE OF CHANCE
The earliest cycle day for which our model has high accuracy to predict both trigger day and risk of over-response is stimulation Day 5. The Day 5 model to predict trigger date has a mean squared error 2.16 ± 0.12 and to predict over-response an area under the receiver operating characteristic curve 0.91 ± 0.01.

LIMITATIONS, REASONS FOR CAUTION
This is a retrospective single-centre study and the results may not be generalisable to centres using different treatment protocols. The results are derived from modelling, and further clinical validation studies will verify the accuracy of the model.

WIDER IMPLICATIONS OF THE FINDINGS
Follicular tracking starting at Day 5 of stimulation may help to streamline the amount of monitoring required in COS. Previous small studies have shown that minimal monitoring protocols did not adversely impact outcomes. If IVF can safely be made less onerous on the clinic’s resources and patient’s time, without compromising success, this could help to reduce burden-related treatment drop-out.

STUDY FUNDING/COMPETING INTEREST(S)
F.P.C. acknowledges funding from the NIHR Applied Research Collaboration Wessex. The authors declare they have no competing interests in relation to this work.

TRIAL REGISTRATION NUMBER
N/A.

Text
Scan streamlining - Accepted Manuscript
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More information

Submitted date: 1 August 2020
Accepted/In Press date: 4 September 2020
e-pub ahead of print date: 4 November 2020

Identifiers

Local EPrints ID: 444141
URI: http://eprints.soton.ac.uk/id/eprint/444141
ISSN: 1460-2350
PURE UUID: fe9ff166-969f-485a-b97a-60fd28ee1c9b
ORCID for Ying Cheong: ORCID iD orcid.org/0000-0001-7687-4597

Catalogue record

Date deposited: 29 Sep 2020 16:30
Last modified: 26 Nov 2021 05:38

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Contributors

Author: Isla Robertson
Author: Francis Chmiel
Author: Ying Cheong ORCID iD

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